scREAD: A single-cell RNA-Seq database for Alzheimer’s Disease

Jiang, Jing; Wang, Cankun; Qi, Ren; Fu, Hongjun; Ma, Qin

doi:10.1101/2020.08.06.240044

Cited by 5 publications

(3 citation statements)

References 19 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, a real-time ssREAD server status page is available at https://ssread.statuspage.io/. The source code to the version of the code used in this study is also available on Zenodo 67 . Author Contributions Statement Q.M.…”

Section: Data Availabilitymentioning

confidence: 99%

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Wang,

Acosta,

McNutt

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Alzheimer’s Disease (AD) is a neurodegenerative malady predominantly affecting the elderly and exhibits its debilitating effects on a dementia-prone population. Recently, the advent of innovative technologies, such as single-cell and single-nucleus RNA-sequencing (scRNA-seq & snRNA-seq) and spatial transcriptomics (ST), has reformed our investigative approaches toward comprehending AD’s neuropathological intricacies and underpinning regulatory mechanisms, encompassing sub-cellular, cellular, and spatial dimensions. In light of the overwhelming proliferation of single-cell and ST data associated with AD, the imperative for a comprehensive, user-friendly database that addresses the scientific community’s analytical demands has never been more paramount. Introduced initially in 2020, scREAD presented itself as a pioneering repository that systematized publicly available scRNA-seq and snRNA-seq datasets derived from post-mortem human brain tissues and mouse models mirroring AD pathology. Here, we introduce ssREAD, a substantial upgrade over scREAD, enriching the platform with a broader spectrum of datasets, an optimized analytical pipeline, and enhanced usability and visibility. Specifically, ssREAD amalgamates an impressive portfolio of over 189 datasets extracted from 35 distinct AD-related scRNA-seq and snRNA-seq studies, encompassing a staggering 2,572,355 cells. In addition, we have diligently curated and archived 300 ST datasets, originating from 12 human and mouse brain studies, which include two focused on AD and ten control studies. Every dataset within our repository is meticulously annotated, bearing critical identifiers including species, gender, brain region, disease/control status, age, and AD stages. Besides the collection of above datasets in ssREAD, it delivers an exhaustive analysis suite offering cell clustering and annotation, inference of differentially expressed and spatially variable genes, identification of cell-type-specific marker genes and regulons, and spot deconvolution for integrative analysis of ST and scRNA-seq & snRNA-seq data from public domains. All these resources are freely accessible through a user-friendly, consolidated web portal available athttps://bmblx.bmi.osumc.edu/ssread/.

show abstract

Section: Data Availabilitymentioning

confidence: 99%

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Wang,

Acosta,

McNutt

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…11. To reveal critical cell-type distribution ( i.e., excitatory neuron) associated with selective neuronal vulnerability in AD 17 , five well-defined excitatory neuron marker genes (SLC17A6, SLC17A7, NRGN, CAMK2A, and SATB2) in the cortex were obtained from our in-house database scREAD 18 (other cell-type marker genes in Supplementary Table 3 ). The module score and optimized RGB image ( Fig.…”

Section: Mainmentioning

confidence: 99%

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

Chang

Wang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics by reconstructing and segmenting a transcriptome mapped RGB image. RESEPT can identify the tissue architecture, and represent corresponding marker genes and biological functions accurately. RESEPT also provides critical insights into the underlying mechanisms driving the complex tissue heterogeneities in Alzheimer’s disease and glioblastoma.

show abstract

“…The function FindAllMarkers was utilized to identify the EC dataset's DEGs based on benchmarking labels (Supplementary Table 9). An in-house database named scREAD 72 was used for acquiring celltype-specific markers. Final cell-type-specific markers (CTS, Supplementary Table…”

Section: Spatial Integration Of Snrna-seq and St Data From Human Mtgmentioning

confidence: 99%

Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer’s pathology

Chen

Chang

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Alzheimer's disease (AD) is pathologically characterized by amyloid beta (Aβ) plaques, neurofibrillary tangles (tau aggregates), and alterations in microglia, astrocytes and oligodendrocytes. The mesial temporal lobe is a vulnerable brain region in early AD; however, little is known about the transcriptome-scale gene expression in this region and its relation to AD pathology. Here we use the 10x Genomics Visium platform in combination with co-immunofluorescence staining of AD-associated pathological markers to define the spatial topography of gene expression in the middle temporal gyrus (MTG) from both early AD and age- and gender-matched control cases. We identify unique marker genes for six cortical layers and the adjacent white matter as well as gene expression patterns and alterations that showcase unique gene signatures and pathways associated with a range of AD pathology. Also, gene co-expression analyses of differentially expressed genes (DEGs) between AD and controls reveal four unique gene modules, which significantly change their co-expression patterns in the presence of variations of AD pathology. Furthermore, we validate the changes of key representative DEGs that are associated with AD pathology in neurons, microglia, astrocytes and oligodendrocytes using single-molecule fluorescent in situ hybridization. In summary, we provide a rich resource for the spatial transcriptomic profile of the human MTG, which will contribute to our understanding of the complex architecture and AD pathology of this vulnerable brain region.

show abstract

scREAD: A single-cell RNA-Seq database for Alzheimer’s Disease

Cited by 5 publications

References 19 publications

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

A Single-cell and Spatial RNA-seq Database for Alzheimer’s Disease (ssREAD)

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer’s pathology

Contact Info

Product

Resources

About